{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3bbebfa3",
   "metadata": {},
   "source": [
    "# Attention-based GRN Inference on Fine-tuned Model\n",
    "Here we use the fine-tuned blood model on the Adamson perturbation dataset as an example of the cell-state specific GRN inference via attention weights. scGPT outputs attention weights on the individual cell level, which can be further aggregated by cell states. In this particular example, we compare the most influenced genes between a transcription factor repression condition (perturbed) and the control. However, this attention-based GRN inference is not restricted to perturbation-based discoveries. It can also be used to compare between cell states in general, such as healthy v.s. diseased, undifferentiated v.s. differentiated, as a broader application.\n",
    "\n",
    "Users may perform scGPT's attention-based GRN inference in the following steps:\n",
    "\n",
    "     1. Load fine-tuned scGPT model and data\n",
    "     \n",
    "     2. Retrieve scGPT's attention weights by condition (i.e., cell states)\n",
    "     \n",
    "     3. Perform scGPT's rank-based most influenced gene selection\n",
    "     \n",
    "     4. Validate the most influenced gene list against existing databases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b674f4",
   "metadata": {},
   "source": [
    "NOTE in advance: to run this tutorial notebook, you may need to download the fine-tuned model from [link](https://drive.google.com/drive/folders/1HsPrwYGPXm867_u_Ye0W4Ch8AFSneXAn) and the list of targets of BHLHE40 from CHIP-Atlas for evaluation from [link](https://drive.google.com/drive/folders/1nc1LywRHlzt4Z17McfXiqBWgoGbRNyc0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8a59dea6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Global seed set to 0\n",
      "/h/chloexq/.cache/pypoetry/virtualenvs/scgpt--qSLVbd1-py3.9/lib/python3.9/site-packages/flatbuffers/compat.py:19: DeprecationWarning: the imp module is deprecated in favour of importlib; see the module's documentation for alternative uses\n",
      "  import imp\n"
     ]
    }
   ],
   "source": [
    "import copy\n",
    "import json\n",
    "import os\n",
    "from pathlib import Path\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import torch\n",
    "from anndata import AnnData\n",
    "import scanpy as sc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import tqdm\n",
    "import gseapy as gp\n",
    "from gears import PertData, GEARS\n",
    "\n",
    "from scipy.sparse import issparse\n",
    "import scipy as sp\n",
    "from einops import rearrange\n",
    "from torch.nn.functional import softmax\n",
    "from tqdm import tqdm\n",
    "import pandas as pd\n",
    "\n",
    "from torchtext.vocab import Vocab\n",
    "from torchtext._torchtext import (\n",
    "    Vocab as VocabPybind,\n",
    ")\n",
    "\n",
    "sys.path.insert(0, \"../\")\n",
    "\n",
    "import scgpt as scg\n",
    "from scgpt.tasks import GeneEmbedding\n",
    "from scgpt.tokenizer.gene_tokenizer import GeneVocab\n",
    "from scgpt.model import TransformerModel\n",
    "from scgpt.utils import set_seed \n",
    "from scgpt.tokenizer import tokenize_and_pad_batch\n",
    "from scgpt.preprocess import Preprocessor\n",
    "\n",
    "os.environ[\"KMP_WARNINGS\"] = \"off\"\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d846b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seed(42)\n",
    "pad_token = \"<pad>\"\n",
    "special_tokens = [pad_token, \"<cls>\", \"<eoc>\"]\n",
    "n_hvg = 1200\n",
    "n_bins = 51\n",
    "mask_value = -1\n",
    "pad_value = -2\n",
    "n_input_bins = n_bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c246caf",
   "metadata": {},
   "source": [
    "## Step 1: Load fine-tuned model and dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8e73800",
   "metadata": {},
   "source": [
    "### 1.1  Load fine-tuned model\n",
    "\n",
    "We are going to load a fine-tuned model for the gene interaction analysis on Adamson dataset. The fine-tuned model can be downloaded via this [link](https://drive.google.com/drive/folders/1HsPrwYGPXm867_u_Ye0W4Ch8AFSneXAn). The dataset will be loaded in the next step 1.2.\n",
    "\n",
    "To reproduce the provided fine-tuned model. Please followw the integration fin-tuning pipeline to fine-tune the pre-trained blood model on the Adamson perturbation dataset. Note that in the fine-tuning stage, we did not perform highly vairable gene selection but trained on the 5000+ genes present in the Adamson dataset. This is to provide flexbility in the inference stage to investigate changes in attention maps across different perturbation conditions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7a45d1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resume model from ../save/finetuned_scGPT_adamson/best_model.pt, the model args will override the config ../save/finetuned_scGPT_adamson/args.json.\n"
     ]
    }
   ],
   "source": [
    "# Specify model path; here we load the scGPT blood model fine-tuned on adamson\n",
    "model_dir = Path(\"../save/finetuned_scGPT_adamson\")\n",
    "model_config_file = model_dir / \"args.json\"\n",
    "model_file = model_dir / \"best_model.pt\"\n",
    "vocab_file = model_dir / \"vocab.json\"\n",
    "\n",
    "vocab = GeneVocab.from_file(vocab_file)\n",
    "for s in special_tokens:\n",
    "    if s not in vocab:\n",
    "        vocab.append_token(s)\n",
    "\n",
    "# Retrieve model parameters from config files\n",
    "with open(model_config_file, \"r\") as f:\n",
    "    model_configs = json.load(f)\n",
    "print(\n",
    "    f\"Resume model from {model_file}, the model args will override the \"\n",
    "    f\"config {model_config_file}.\"\n",
    ")\n",
    "embsize = model_configs[\"embsize\"]\n",
    "nhead = model_configs[\"nheads\"]\n",
    "d_hid = model_configs[\"d_hid\"]\n",
    "nlayers = model_configs[\"nlayers\"]\n",
    "n_layers_cls = model_configs[\"n_layers_cls\"]\n",
    "\n",
    "gene2idx = vocab.get_stoi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fed785b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using simple batchnorm instead of domain specific batchnorm\n",
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    },
    {
     "data": {
      "text/plain": [
       "TransformerModel(\n",
       "  (encoder): GeneEncoder(\n",
       "    (embedding): Embedding(36574, 512, padding_idx=36571)\n",
       "    (enc_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (value_encoder): ContinuousValueEncoder(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "    (linear1): Linear(in_features=1, out_features=512, bias=True)\n",
       "    (activation): ReLU()\n",
       "    (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "    (norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (bn): BatchNorm1d(512, eps=6.1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "  (transformer_encoder): TransformerEncoder(\n",
       "    (layers): ModuleList(\n",
       "      (0): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (1): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (2): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (3): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (4): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (5): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (6): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (7): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (8): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (9): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (10): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "      (11): FlashTransformerEncoderLayer(\n",
       "        (self_attn): FlashMHA(\n",
       "          (Wqkv): Linear(in_features=512, out_features=1536, bias=True)\n",
       "          (inner_attn): FlashAttention()\n",
       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
       "        )\n",
       "        (linear1): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (dropout): Dropout(p=0.5, inplace=False)\n",
       "        (linear2): Linear(in_features=512, out_features=512, bias=True)\n",
       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        (dropout1): Dropout(p=0.5, inplace=False)\n",
       "        (dropout2): Dropout(p=0.5, inplace=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (decoder): ExprDecoder(\n",
       "    (fc): Sequential(\n",
       "      (0): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (1): LeakyReLU(negative_slope=0.01)\n",
       "      (2): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (3): LeakyReLU(negative_slope=0.01)\n",
       "      (4): Linear(in_features=512, out_features=1, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (cls_decoder): ClsDecoder(\n",
       "    (_decoder): ModuleList(\n",
       "      (0): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (1): ReLU()\n",
       "      (2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (3): Linear(in_features=512, out_features=512, bias=True)\n",
       "      (4): ReLU()\n",
       "      (5): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "    )\n",
       "    (out_layer): Linear(in_features=512, out_features=1, bias=True)\n",
       "  )\n",
       "  (sim): Similarity(\n",
       "    (cos): CosineSimilarity()\n",
       "  )\n",
       "  (creterion_cce): CrossEntropyLoss()\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "ntokens = len(vocab)  # size of vocabulary\n",
    "model = TransformerModel(\n",
    "    ntokens,\n",
    "    embsize,\n",
    "    nhead,\n",
    "    d_hid,\n",
    "    nlayers,\n",
    "    vocab=vocab,\n",
    "    pad_value=pad_value,\n",
    "    n_input_bins=n_input_bins,\n",
    "    use_fast_transformer=True,\n",
    ")\n",
    "\n",
    "try:\n",
    "    model.load_state_dict(torch.load(model_file))\n",
    "    print(f\"Loading all model params from {model_file}\")\n",
    "except:\n",
    "    # only load params that are in the model and match the size\n",
    "    model_dict = model.state_dict()\n",
    "    pretrained_dict = torch.load(model_file)\n",
    "    pretrained_dict = {\n",
    "        k: v\n",
    "        for k, v in pretrained_dict.items()\n",
    "        if k in model_dict and v.shape == model_dict[k].shape\n",
    "    }\n",
    "    for k, v in pretrained_dict.items():\n",
    "        print(f\"Loading params {k} with shape {v.shape}\")\n",
    "        model_dict.update(pretrained_dict)\n",
    "        model.load_state_dict(model_dict)\n",
    "\n",
    "model.to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdda8f4",
   "metadata": {},
   "source": [
    "### 1.2  Load dataset of interest\n",
    "The Adamson perturbation dataset is retrieved from the GEARS package with the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "de3c1d15",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Local copy of pyg dataset is detected. Loading...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "data_dir = Path(\"../data\")\n",
    "pert_data = PertData(data_dir)\n",
    "pert_data.load(data_name=\"adamson\")\n",
    "adata = sc.read(data_dir / \"adamson/perturb_processed.h5ad\")\n",
    "ori_batch_col = \"control\"\n",
    "adata.obs[\"celltype\"] = adata.obs[\"condition\"].astype(\"category\")\n",
    "adata.obs[\"str_batch\"] = adata.obs[\"control\"].astype(str)\n",
    "data_is_raw = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b0feb668",
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.var[\"id_in_vocab\"] = [1 if gene in vocab else -1 for gene in adata.var[\"gene_name\"]]\n",
    "gene_ids_in_vocab = np.array(adata.var[\"id_in_vocab\"])\n",
    "adata = adata[:, adata.var[\"id_in_vocab\"] >= 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eda94662",
   "metadata": {},
   "source": [
    "In the scGPT workflow, we compare each TF perturbation condition with control one at a time. In each run, the data is subsetted to contain one TF and control only. In this example, we use the TF BHLHE40."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d99c7d82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BHLHE40+ctrl', 'ctrl'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TF_name = 'BHLHE40'\n",
    "adata = adata[adata.obs.condition.isin(['{}+ctrl'.format(TF_name), 'ctrl'])].copy()\n",
    "np.unique(adata.obs.condition)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a8d08c",
   "metadata": {},
   "source": [
    "We further pre-process the subsetted data following the scGPT pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b81799b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scGPT - INFO - Filtering genes by counts ...\n",
      "scGPT - INFO - Filtering cells by counts ...\n",
      "scGPT - INFO - Normalizing total counts ...\n",
      "scGPT - INFO - Binning data ...\n"
     ]
    }
   ],
   "source": [
    "preprocessor = Preprocessor(\n",
    "    use_key=\"X\",  # the key in adata.layers to use as raw data\n",
    "    filter_gene_by_counts=3,  # step 1\n",
    "    filter_cell_by_counts=False,  # step 2\n",
    "    normalize_total=1e4,  # 3. whether to normalize the raw data and to what sum\n",
    "    result_normed_key=\"X_normed\",  # the key in adata.layers to store the normalized data\n",
    "    log1p=data_is_raw,  # 4. whether to log1p the normalized data\n",
    "    result_log1p_key=\"X_log1p\",\n",
    "    subset_hvg= False,  # 5. whether to subset the raw data to highly variable genes\n",
    "    hvg_flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n",
    "    binning=n_bins,  # 6. whether to bin the raw data and to what number of bins\n",
    "    result_binned_key=\"X_binned\",  # the key in adata.layers to store the binned data\n",
    ")\n",
    "preprocessor(adata, batch_key=\"str_batch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da616114",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AnnData object with n_obs × n_vars = 24767 × 1200\n",
      "    obs: 'condition', 'cell_type', 'dose_val', 'control', 'condition_name', 'celltype', 'str_batch', 'n_counts'\n",
      "    var: 'gene_name', 'id_in_vocab', 'n_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm', 'highly_variable_nbatches', 'highly_variable_intersection'\n",
      "    uns: 'non_dropout_gene_idx', 'non_zeros_gene_idx', 'rank_genes_groups_cov_all', 'top_non_dropout_de_20', 'top_non_zero_de_20', 'hvg'\n",
      "    obsm: 'bin_edges'\n",
      "    layers: 'X_normed', 'X_binned'\n"
     ]
    }
   ],
   "source": [
    "sc.pp.highly_variable_genes(\n",
    "    adata,\n",
    "    layer=None,\n",
    "    n_top_genes=1200,\n",
    "    batch_key=\"str_batch\",\n",
    "    flavor=\"seurat_v3\" if data_is_raw else \"cell_ranger\",\n",
    "    subset=False,\n",
    ")\n",
    "adata.var.loc[adata.var[adata.var.gene_name==TF_name].index, 'highly_variable'] = True\n",
    "adata = adata[:, adata.var[\"highly_variable\"]].copy()\n",
    "print(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd3ffd5",
   "metadata": {},
   "source": [
    "## Step 2: Retrieve scGPT's attention weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cc26b6",
   "metadata": {},
   "source": [
    "### 2.1 Prepare model input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5639266",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_layer_key = \"X_binned\"\n",
    "all_counts = (\n",
    "    adata.layers[input_layer_key].toarray()\n",
    "    if issparse(adata.layers[input_layer_key])\n",
    "    else adata.layers[input_layer_key]\n",
    ")\n",
    "genes = adata.var[\"gene_name\"].tolist()\n",
    "gene_ids = np.array(vocab(genes), dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "83bc0468",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 16\n",
    "tokenized_all = tokenize_and_pad_batch(\n",
    "    all_counts,\n",
    "    gene_ids,\n",
    "    max_len=len(genes)+1,\n",
    "    vocab=vocab,\n",
    "    pad_token=pad_token,\n",
    "    pad_value=pad_value,\n",
    "    append_cls=True,  # append <cls> token at the beginning\n",
    "    include_zero_gene=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5b4bea9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_gene_ids, all_values = tokenized_all[\"genes\"], tokenized_all[\"values\"]\n",
    "src_key_padding_mask = all_gene_ids.eq(vocab[pad_token])\n",
    "condition_ids = np.array(adata.obs[\"condition\"].tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6661cd8",
   "metadata": {},
   "source": [
    "### 2.1 Retrieve attention weights\n",
    "Note that since the flash-attn package does not output attention scores, we manually calculate q @ k.T to extract the attention weights. Users may specify which layer to extract the attention weights from. In the manuscript, we used the attention weights from the last (12th) layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bb6993a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()\n",
    "dict_sum_condition = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "91bd96f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this argument to specify which layer to extract the attention weights from\n",
    "# Default to 11, extraction from the last (12th) layer. Note that index starts from 0\n",
    "num_attn_layers = 11 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53301b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1548/1548 [08:56<00:00,  2.88it/s]\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "with torch.no_grad(), torch.cuda.amp.autocast(enabled=True):\n",
    "    M = all_gene_ids.size(1)\n",
    "    N = all_gene_ids.size(0)\n",
    "    device = next(model.parameters()).device\n",
    "    for i in tqdm(range(0, N, batch_size)):\n",
    "        batch_size = all_gene_ids[i : i + batch_size].size(0)\n",
    "        outputs = np.zeros((batch_size, M, M), dtype=np.float32)\n",
    "        # Replicate the operations in model forward pass\n",
    "        src_embs = model.encoder(torch.tensor(all_gene_ids[i : i + batch_size], dtype=torch.long).to(device))\n",
    "        val_embs = model.value_encoder(torch.tensor(all_values[i : i + batch_size], dtype=torch.float).to(device))\n",
    "        total_embs = src_embs + val_embs\n",
    "        total_embs = model.bn(total_embs.permute(0, 2, 1)).permute(0, 2, 1)\n",
    "        # Send total_embs to attention layers for attention operations\n",
    "        # Retrieve the output from second to last layer\n",
    "        for layer in model.transformer_encoder.layers[:num_attn_layers]:\n",
    "            total_embs = layer(total_embs, src_key_padding_mask=src_key_padding_mask[i : i + batch_size].to(device))\n",
    "        # Send total_embs to the last layer in flash-attn\n",
    "        # https://github.com/HazyResearch/flash-attention/blob/1b18f1b7a133c20904c096b8b222a0916e1b3d37/flash_attn/flash_attention.py#L90\n",
    "        qkv = model.transformer_encoder.layers[num_attn_layers].self_attn.Wqkv(total_embs)\n",
    "        # Retrieve q, k, and v from flast-attn wrapper\n",
    "        qkv = rearrange(qkv, 'b s (three h d) -> b s three h d', three=3, h=8)\n",
    "        q = qkv[:, :, 0, :, :]\n",
    "        k = qkv[:, :, 1, :, :]\n",
    "        v = qkv[:, :, 2, :, :]\n",
    "        # https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a\n",
    "        # q = [batch, gene, n_heads, n_hid]\n",
    "        # k = [batch, gene, n_heads, n_hid]\n",
    "        # attn_scores = [batch, n_heads, gene, gene]\n",
    "        attn_scores = q.permute(0, 2, 1, 3) @ k.permute(0, 2, 3, 1)\n",
    "        # Rank normalization by row\n",
    "        attn_scores = attn_scores.reshape((-1, M))\n",
    "        order = torch.argsort(attn_scores, dim=1)\n",
    "        rank = torch.argsort(order, dim=1)\n",
    "        attn_scores = rank.reshape((-1, 8, M, M))/M\n",
    "        # Rank normalization by column\n",
    "        attn_scores = attn_scores.permute(0, 1, 3, 2).reshape((-1, M))\n",
    "        order = torch.argsort(attn_scores, dim=1)\n",
    "        rank = torch.argsort(order, dim=1)\n",
    "        attn_scores = (rank.reshape((-1, 8, M, M))/M).permute(0, 1, 3, 2)\n",
    "        # Average 8 attention heads\n",
    "        attn_scores = attn_scores.mean(1)\n",
    "        \n",
    "        outputs = attn_scores.detach().cpu().numpy()\n",
    "        \n",
    "        for index in range(batch_size):\n",
    "            # Keep track of sum per condition\n",
    "            c = condition_ids[i : i + batch_size][index]\n",
    "            if c not in dict_sum_condition:\n",
    "                dict_sum_condition[c] = np.zeros((M, M), dtype=np.float32)\n",
    "            else:\n",
    "                dict_sum_condition[c] += outputs[index, :, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "571d16b4",
   "metadata": {},
   "source": [
    "### 2.2 Average rank-normed attention weights by condition\n",
    "In the previous step, we retrieve the attention weights for all cells and keep the running sum by condition (i.e., control, perturbed). We further calculate the mean here by dividing the number of cells per condition to obtain a gene * gene attention matrix for each condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2544f5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = adata.obs.groupby('condition').groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8485f055",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_sum_condition_mean = dict_sum_condition.copy()\n",
    "for i in groups.keys():\n",
    "    dict_sum_condition_mean[i] = dict_sum_condition_mean[i]/len(groups[i])\n",
    "gene_vocab_idx = all_gene_ids[0].clone().detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b23f521",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ctrl': array([[0.7667165 , 0.5071312 , 0.363181  , ..., 0.6558552 , 0.6433118 ,\n",
       "         0.5029277 ],\n",
       "        [0.35063276, 0.90670127, 0.32875505, ..., 0.67563516, 0.4646933 ,\n",
       "         0.4744476 ],\n",
       "        [0.5349599 , 0.30492574, 0.63999563, ..., 0.37222496, 0.45482123,\n",
       "         0.4780819 ],\n",
       "        ...,\n",
       "        [0.5522125 , 0.72981244, 0.347227  , ..., 0.7278823 , 0.5751889 ,\n",
       "         0.48467204],\n",
       "        [0.5728605 , 0.54362035, 0.49283794, ..., 0.5536558 , 0.71726304,\n",
       "         0.59353757],\n",
       "        [0.646479  , 0.52209556, 0.410678  , ..., 0.5902157 , 0.6444179 ,\n",
       "         0.54989314]], dtype=float32),\n",
       " 'BHLHE40+ctrl': array([[0.6798215 , 0.43131036, 0.39218244, ..., 0.67530143, 0.6967861 ,\n",
       "         0.5009627 ],\n",
       "        [0.28934428, 0.809951  , 0.30970335, ..., 0.61639196, 0.26737106,\n",
       "         0.2312922 ],\n",
       "        [0.5355754 , 0.28648922, 0.6779555 , ..., 0.3633715 , 0.51926744,\n",
       "         0.59302956],\n",
       "        ...,\n",
       "        [0.5082808 , 0.69850725, 0.34680215, ..., 0.75059116, 0.61094743,\n",
       "         0.42005002],\n",
       "        [0.60800856, 0.47265798, 0.58815396, ..., 0.54617417, 0.76434106,\n",
       "         0.635293  ],\n",
       "        [0.67291903, 0.40639865, 0.5096317 , ..., 0.52571416, 0.6641971 ,\n",
       "         0.5768942 ]], dtype=float32)}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_sum_condition_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "066e8d9a",
   "metadata": {},
   "source": [
    "## Step 3: Perform most influenced gene selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b89dbcf",
   "metadata": {},
   "source": [
    "In the manuscript, we proposed 3 settings for the most influenced gene selection, namely *Control*, *Perturb*, and *Difference*. In this example, we focus on the *Difference* setting to explore how the gene-gene network changes after perturbation compared to control."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c9c4abeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_topk_most_influenced_genes(topk, setting):\n",
    "    attn_top_gene_dict = {}\n",
    "    attn_top_scores_dict = {}\n",
    "    for i in groups.keys():\n",
    "        if i != 'ctrl':\n",
    "            knockout_gene = i.split('+')[0]\n",
    "            knockout_gene_idx = np.where(gene_vocab_idx==vocab([knockout_gene])[0])[0][0]\n",
    "            control = dict_sum_condition_mean['ctrl'][:, knockout_gene_idx]\n",
    "            exp = dict_sum_condition_mean[i][:, knockout_gene_idx]\n",
    "            # Chnage this line to exp, control, exp-control for three different settings\n",
    "            if setting == 'difference':\n",
    "                a = exp-control\n",
    "            elif setting == 'control':\n",
    "                a = control\n",
    "            elif setting == 'experiment':\n",
    "                a = exp\n",
    "            diff_idx = np.argpartition(a, -topk)[-topk:]\n",
    "            scores = (a)[diff_idx]\n",
    "            attn_top_genes = vocab.lookup_tokens(gene_vocab_idx[diff_idx]) + [TF_name]\n",
    "            attn_top_gene_dict[i] = list(attn_top_genes)\n",
    "            attn_top_scores_dict[i] = list(scores)\n",
    "    return attn_top_gene_dict, attn_top_scores_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cceab4f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_vocab_idx = all_gene_ids[0].clone().detach().cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd2a086b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.27003407, 0.27020293, 0.27515042, 0.28356528, 0.2868172, 0.2719887, 0.27392942, 0.28411555, 0.27914363, 0.27320516, 0.27022785, 0.2799158, 0.27595383, 0.27598792, 0.27211004, 0.28957933, 0.29275084, 0.2942825, 0.30242187, 0.29776084]\n",
      "['SH3BGRL3', 'HBA1', 'PSMD4', 'FKBP2', 'HSPA8', 'HNRNPD', 'NDUFB6', 'RTN4', 'DNAJA1', 'SSR2', 'NENF', 'UQCRB', 'HSP90B1', 'PRRC2C', 'ADRM1', 'DBI', 'HSPB1', 'HMGN3', 'TMBIM6', 'ACAT2', 'BHLHE40']\n"
     ]
    }
   ],
   "source": [
    "# Specify top k number of genes to be selected, and the selection setting\n",
    "# Here calculate top 20 most influenced genes for CHIP-Atlas validation\n",
    "topk = 20\n",
    "setting = 'difference' # \"control\", \"perturbed\"\n",
    "assert setting in [\"difference\", \"control\", \"perturbed\"]\n",
    "attn_top_gene_dict_20, attn_top_scores_dict_20 = get_topk_most_influenced_genes(topk, setting)\n",
    "print(attn_top_scores_dict_20[TF_name + '+ctrl'])\n",
    "print(attn_top_gene_dict_20[TF_name + '+ctrl'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "802bcd2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if setting == 'difference':\n",
    "    for i in attn_top_gene_dict_20.keys():\n",
    "        example_genes = attn_top_gene_dict_20[i]\n",
    "        gene_idx = [np.where(gene_vocab_idx==vocab([g])[0])[0][0] for g in example_genes]\n",
    "        scores = dict_sum_condition_mean[i][gene_idx, :][:, gene_idx]-dict_sum_condition_mean['ctrl'][gene_idx, :][:, gene_idx]\n",
    "        df_scores = pd.DataFrame(data = scores, columns = example_genes, index = example_genes)\n",
    "        plt.figure(figsize=(6, 6), dpi=300)\n",
    "        ax = sns.clustermap(df_scores, annot=False, cmap=sns.diverging_palette(145, 300, s=60, as_cmap=True), fmt='.2f', vmin=-0.3, vmax=0.3) \n",
    "        plt.show()\n",
    "        plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9b0693fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.13435426, 0.13445804, 0.13521338, 0.13658851, 0.13494265, 0.1378941, 0.25253183, 0.27598792, 0.27320516, 0.19422996, 0.1749267, 0.14947963, 0.28356528, 0.19787788, 0.21807176, 0.27211004, 0.19592029, 0.192339, 0.26201934, 0.27022785, 0.18904561, 0.2630661, 0.17665666, 0.25517708, 0.1592493, 0.28411555, 0.15643954, 0.17658818, 0.28957933, 0.23216242, 0.27515042, 0.17963398, 0.17776287, 0.21403193, 0.1494323, 0.26097095, 0.2147252, 0.2868172, 0.1864397, 0.19282275, 0.17112768, 0.18815774, 0.19837373, 0.14006501, 0.15750575, 0.22382861, 0.2719887, 0.25040454, 0.25988054, 0.26159328, 0.14806324, 0.20562506, 0.26052994, 0.20960271, 0.17409635, 0.22525501, 0.26490605, 0.20550716, 0.15476418, 0.14542323, 0.30242187, 0.16769892, 0.1891504, 0.16443193, 0.2682315, 0.25884175, 0.25024498, 0.26073533, 0.15007758, 0.27020293, 0.27914363, 0.24032599, 0.16723317, 0.18915558, 0.27392942, 0.15589827, 0.1956358, 0.15076661, 0.14257717, 0.2255286, 0.18222451, 0.2942825, 0.18665028, 0.29776084, 0.29275084, 0.27003407, 0.14769578, 0.15558112, 0.24545509, 0.23430616, 0.23824137, 0.2799158, 0.27595383, 0.23613232, 0.17815387, 0.25799888, 0.2626958, 0.23995376, 0.19605911, 0.22703129]\n",
      "['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PCNA', 'IFITM1', 'MS4A4A', 'PRRC2C', 'SSR2', 'HAX1', 'ASRGL1', 'SLC3A2', 'FKBP2', 'CFD', 'PRSS57', 'ADRM1', 'NEAT1', 'GMFG', 'CCDC85B', 'NENF', 'BLVRB', 'PARP1', 'SMYD3', 'DNAJC1', 'PTPRCAP', 'RTN4', 'RP11-660L16.2', 'MTHFD2', 'DBI', 'VIM', 'PSMD4', 'MLLT11', 'C2orf88', 'IDI1', 'PSMD12', 'DDX5', 'HMBS', 'HSPA8', 'HOXB2', 'MANF', 'CHI3L2', 'HSPA5', 'LMO2', 'GATA2', 'PNMT', 'TMEM97', 'HNRNPD', 'SNHG8', 'GYPB', 'GYPA', 'MVD', 'UBE2S', 'PITX1', 'IFITM2', 'CLCA1', 'PTTG1', 'FUS', 'CKS2', 'SPN', 'SSR1', 'TMBIM6', 'ATF7IP2', 'CARHSP1', 'XBP1', 'SRRM2', 'GSTO1', 'NPW', 'SFPQ', 'TMEM54', 'HBA1', 'DNAJA1', 'LAPTM5', 'AIF1', 'CTSH', 'NDUFB6', 'BRD2', 'LGALS1', 'DDX17', 'MCM3', 'EEF1A1', 'TSPO', 'HMGN3', 'CITED2', 'ACAT2', 'HSPB1', 'SH3BGRL3', 'PHLDA2', 'STXBP6', 'MYC', 'BTG1', 'RGS10', 'UQCRB', 'HSP90B1', 'TIMP1', 'LCP1', 'EXOSC8', 'UBC', 'BEX4', 'RPL39', 'MYL4', 'BHLHE40']\n"
     ]
    }
   ],
   "source": [
    "# Specify top k number of genes to be selected, and the selection setting\n",
    "# # Here calculate top 100 most influenced genes for pathway validation\n",
    "topk = 100\n",
    "setting = 'difference' # \"control\", \"perturbed\"\n",
    "assert setting in [\"difference\", \"control\", \"perturbed\"]\n",
    "attn_top_gene_dict_100, attn_top_scores_dict_100 = get_topk_most_influenced_genes(topk, setting)\n",
    "print(attn_top_scores_dict_100[TF_name + '+ctrl'])\n",
    "print(attn_top_gene_dict_100[TF_name + '+ctrl'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db5083ac",
   "metadata": {},
   "source": [
    "## Step 4: Validate most influenced genes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025cde91",
   "metadata": {},
   "source": [
    "### Step 4.1: Validate against CHIP-Atlas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10e8242",
   "metadata": {},
   "source": [
    "First load the tsv file from CHIP-Atlas containing targets of BHLHE40. The tsv file for BHLHE40 can be downloaded via this [link](https://drive.google.com/drive/folders/1nc1LywRHlzt4Z17McfXiqBWgoGbRNyc0). This tsv file was originally retrieved from the [CHIP-Atlas](https://chip-atlas.org/target_genes) website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3089cb62",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./reference/BHLHE40.10.tsv', delimiter='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fa4ada4",
   "metadata": {},
   "source": [
    "Examine the overalp between the selected genes (top 20) and known target genes from the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dcd587ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'ACAT2',\n",
       "  'ADRM1',\n",
       "  'DBI',\n",
       "  'DNAJA1',\n",
       "  'FKBP2',\n",
       "  'HBA1',\n",
       "  'HMGN3',\n",
       "  'HNRNPD',\n",
       "  'HSP90B1',\n",
       "  'HSPA8',\n",
       "  'HSPB1',\n",
       "  'NDUFB6',\n",
       "  'PRRC2C',\n",
       "  'PSMD4',\n",
       "  'RTN4',\n",
       "  'SH3BGRL3',\n",
       "  'SSR2',\n",
       "  'TMBIM6',\n",
       "  'UQCRB'},\n",
       " 19)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gene_list = attn_top_gene_dict_20[TF_name + '+ctrl'][:-1]\n",
    "set(gene_list).intersection(set(df['Target_genes'].values)), len(set(gene_list).intersection(set(df['Target_genes'].values)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50001e4f",
   "metadata": {},
   "source": [
    "Visualize the network and strength of the edges (annotated with rank-normalized attention scores)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9752a485",
   "metadata": {},
   "outputs": [],
   "source": [
    "score_list = attn_top_scores_dict_20[TF_name + '+ctrl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30807b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hits = set(gene_list).intersection(set(df['Target_genes'].values))\n",
    "\n",
    "G = nx.DiGraph()\n",
    "edge_list = [(TF_name, gene_list[i], round(score_list[i], 2)) for i in range(len(gene_list))]\n",
    "G.add_weighted_edges_from(edge_list)\n",
    "\n",
    "plt.figure(figsize=(20, 20))\n",
    "edges = list(G.edges)\n",
    "elarge = [(u, v) for (u, v, d) in G.edges(data=True) if v in hits]\n",
    "esmall = [(u, v) for (u, v, d) in G.edges(data=True) if v not in hits]\n",
    "pos = nx.shell_layout(G)\n",
    "width_large = {}\n",
    "width_small = {}\n",
    "for i, v in enumerate(edges):\n",
    "    if v[1] in hits:\n",
    "        width_large[edges[i]] = G.get_edge_data(v[0], v[1])['weight']*30\n",
    "    else:\n",
    "        width_small[edges[i]] = max(G.get_edge_data(v[0], v[1])['weight'], 0)*30\n",
    "nx.draw_networkx_edges(G, pos,\n",
    "                       edgelist = width_small.keys(),\n",
    "                       width=list(width_small.values()),\n",
    "                       edge_color='grey',\n",
    "                       alpha=0.8)\n",
    "nx.draw_networkx_edges(G, pos, \n",
    "                       edgelist = width_large.keys(), \n",
    "                       width = list(width_large.values()), \n",
    "                       alpha = 0.6, \n",
    "                       edge_color = \"slateblue\",\n",
    "                      )\n",
    "labels = {}\n",
    "for i in pos.keys():\n",
    "    if i == TF_name:\n",
    "        labels[i] = ''\n",
    "    else:\n",
    "        labels[i] = i\n",
    "        \n",
    "labels1 = {}\n",
    "for i in pos.keys():\n",
    "    if i != TF_name:\n",
    "        labels1[i] = ''\n",
    "    else:\n",
    "        labels1[i] = i\n",
    "nx.draw_networkx_labels(G, pos, labels, font_size=30, font_family=\"sans-serif\", horizontalalignment='right')\n",
    "nx.draw_networkx_labels(G, pos, labels1, font_size=30, font_family=\"sans-serif\", font_weight='bold', horizontalalignment='right')\n",
    "\n",
    "d = nx.get_edge_attributes(G, \"weight\")\n",
    "edge_labels = {k: d[k] for k in elarge}\n",
    "nx.draw_networkx_edge_labels(G, pos, edge_labels, font_size=20)\n",
    "ax = plt.gca()\n",
    "ax.margins(0.08)\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c006c98",
   "metadata": {},
   "source": [
    "### Step 4.2: Validate against the Reactome database"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4686dcf3",
   "metadata": {},
   "source": [
    "We perform pathway analysis on the top 100 most influenced genes by checking against the Reactome database. This replicates the reported pathways found in the *Difference* setting in the manuscript for the select TF."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "05ded107",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Validate with Reactome \n",
    "df_database = pd.DataFrame(\n",
    "data = [['GO_Biological_Process_2021', 6036],\n",
    "['GO_Molecular_Function_2021', 1274],\n",
    "['Reactome_2022', 1818]],\n",
    "columns = ['dataset', 'term'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a01e550d",
   "metadata": {},
   "outputs": [],
   "source": [
    "databases = ['Reactome_2022']\n",
    "m = df_database[df_database['dataset'].isin(databases)]['term'].sum() #df_database['term'].sum()\n",
    "p_thresh = 0.05/((len(groups.keys())-1)*m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8de3374a",
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_list = attn_top_gene_dict_100[TF_name + '+ctrl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "acd997e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_attn = pd.DataFrame()\n",
    "enr_Reactome = gp.enrichr(gene_list=gene_list,\n",
    "                           gene_sets=databases,\n",
    "                           organism='Human', \n",
    "                           outdir='test',\n",
    "                           cutoff=0.5)\n",
    "out = enr_Reactome.results\n",
    "out['Gene List'] = str(gene_list)\n",
    "out = out[out['P-value'] < p_thresh]\n",
    "df_attn = df_attn.append(out, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4b556f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_attn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ea5a0ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene_set</th>\n",
       "      <th>Term</th>\n",
       "      <th>Overlap</th>\n",
       "      <th>P-value</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>Old P-value</th>\n",
       "      <th>Old Adjusted P-value</th>\n",
       "      <th>Odds Ratio</th>\n",
       "      <th>Combined Score</th>\n",
       "      <th>Genes</th>\n",
       "      <th>Gene List</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>AUF1 (hnRNP D0) Binds And Destabilizes mRNA R-...</td>\n",
       "      <td>6/54</td>\n",
       "      <td>3.029798e-07</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.119737</td>\n",
       "      <td>392.046796</td>\n",
       "      <td>HSPA8;PSMD12;PSMD4;UBC;HNRNPD;HSPB1</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Regulation Of mRNA Stability By Proteins That ...</td>\n",
       "      <td>7/88</td>\n",
       "      <td>3.082629e-07</td>\n",
       "      <td>0.000087</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.219858</td>\n",
       "      <td>273.157814</td>\n",
       "      <td>HSPA8;PSMD12;PSMD4;EXOSC8;UBC;HNRNPD;HSPB1</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Cellular Responses To Stress R-HSA-2262752</td>\n",
       "      <td>16/722</td>\n",
       "      <td>6.458517e-07</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.117280</td>\n",
       "      <td>72.935042</td>\n",
       "      <td>HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>Cellular Responses To Stimuli R-HSA-8953897</td>\n",
       "      <td>16/736</td>\n",
       "      <td>8.318572e-07</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.014118</td>\n",
       "      <td>70.195667</td>\n",
       "      <td>HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>S Phase R-HSA-69242</td>\n",
       "      <td>7/161</td>\n",
       "      <td>1.753446e-05</td>\n",
       "      <td>0.001971</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.547872</td>\n",
       "      <td>104.562021</td>\n",
       "      <td>PSMD12;PCNA;PSMD4;UBE2S;MYC;UBC;MCM3</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reactome_2022</td>\n",
       "      <td>APC/C:Cdc20 Mediated Degradation Of Securin R-...</td>\n",
       "      <td>5/67</td>\n",
       "      <td>2.239572e-05</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.664147</td>\n",
       "      <td>178.417027</td>\n",
       "      <td>PSMD12;PSMD4;PTTG1;UBE2S;UBC</td>\n",
       "      <td>['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gene_set                                               Term Overlap  \\\n",
       "0  Reactome_2022  AUF1 (hnRNP D0) Binds And Destabilizes mRNA R-...    6/54   \n",
       "1  Reactome_2022  Regulation Of mRNA Stability By Proteins That ...    7/88   \n",
       "2  Reactome_2022         Cellular Responses To Stress R-HSA-2262752  16/722   \n",
       "3  Reactome_2022        Cellular Responses To Stimuli R-HSA-8953897  16/736   \n",
       "4  Reactome_2022                                S Phase R-HSA-69242   7/161   \n",
       "5  Reactome_2022  APC/C:Cdc20 Mediated Degradation Of Securin R-...    5/67   \n",
       "\n",
       "        P-value  Adjusted P-value  Old P-value  Old Adjusted P-value  \\\n",
       "0  3.029798e-07          0.000087            0                     0   \n",
       "1  3.082629e-07          0.000087            0                     0   \n",
       "2  6.458517e-07          0.000117            0                     0   \n",
       "3  8.318572e-07          0.000117            0                     0   \n",
       "4  1.753446e-05          0.001971            0                     0   \n",
       "5  2.239572e-05          0.002098            0                     0   \n",
       "\n",
       "   Odds Ratio  Combined Score  \\\n",
       "0   26.119737      392.046796   \n",
       "1   18.219858      273.157814   \n",
       "2    5.117280       72.935042   \n",
       "3    5.014118       70.195667   \n",
       "4    9.547872      104.562021   \n",
       "5   16.664147      178.417027   \n",
       "\n",
       "                                               Genes  \\\n",
       "0                HSPA8;PSMD12;PSMD4;UBC;HNRNPD;HSPB1   \n",
       "1         HSPA8;PSMD12;PSMD4;EXOSC8;UBC;HNRNPD;HSPB1   \n",
       "2  HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...   \n",
       "3  HSPA8;PSMD12;HSPA5;CITED2;HBA1;HSP90B1;EEF1A1;...   \n",
       "4               PSMD12;PCNA;PSMD4;UBE2S;MYC;UBC;MCM3   \n",
       "5                       PSMD12;PSMD4;PTTG1;UBE2S;UBC   \n",
       "\n",
       "                                           Gene List  \n",
       "0  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  \n",
       "1  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  \n",
       "2  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  \n",
       "3  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  \n",
       "4  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  \n",
       "5  ['IL1RN', 'ARHGDIB', 'COL2A1', 'TRAPPC6A', 'PC...  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_attn"
   ]
  }
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